Defect Separation Distance Analysis for Print Apparatus Diagnosis
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Solution Overview
Problem
In printing, identifying the source of defects in printed images is challenging due to various potential causes, leading to wasted time and inappropriate maintenance, as existing methods lack clear diagnostic tools to distinguish between transient issues and persistent component failures.
Innovation Solution
A computer-implemented method that determines the characteristic separation distance between defects in printed images, allowing for the identification of periodic defects associated with specific print apparatus components, using a processor to analyze scanned images and compare them to reference data, and generating a defect map to determine the source and remedial action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scanned images are compared to reference images to identify defects, then defect detection capability is improved, but the ability to diagnose defect sources and distinguish between transient issues and component failures remains insufficient
Solution Approach 1:
The patent segments defect analysis into multiple diagnostic dimensions: spatial distribution patterns (separation distance, clustering), temporal patterns (occurrence frequency over time), and contextual information (print settings, substrate type). This segmentation enables systematic investigation of defect characteristics to identify root causes while maintaining comprehensive detection capabilities.
Solution Approach 2:
The patent introduces new diagnostic dimensions beyond simple defect detection: spatial dimension (separation distance between defects), temporal dimension (defect occurrence patterns over time), and contextual dimension (print parameters, substrate information). These additional dimensions transform raw defect data into actionable diagnostic information that reveals defect sources and patterns.
2Reliability
If comprehensive defect analysis is performed to identify all potential causes, then diagnostic accuracy is improved, but time for analysis and maintenance increases
Solution Approach 1:
The patent performs preliminary analysis by automatically measuring separation distances between defects, calculating spatial patterns, and comparing against reference data before initiating maintenance procedures. This preliminary computational action filters and pre-processes defect information to identify the most likely causes, enabling faster and more accurate maintenance decisions.
Solution Approach 2:
The patent implements feedback mechanisms where defect analysis results feed back into maintenance decision-making. By continuously monitoring defect patterns, separation distances, and their relationship to print parameters, the system provides feedback that guides targeted maintenance actions, reducing unnecessary maintenance time while improving diagnostic accuracy through iterative learning.
3Difficulty of detecting and measuring
If detailed defect characterization including separation distance is performed, then defect source identification is improved, but processing complexity increases
Solution Approach 1:
The patent extracts specific measurable characteristics from complex defect patterns, particularly separation distance between defects, spatial clustering patterns, and temporal occurrence intervals. By isolating these key measurable parameters, the system simplifies the complex defect analysis process while maintaining the ability to identify defect sources through straightforward comparisons against reference data and established patterns.
Data Source
AI summary
A first separation distance is determined between a first pair of defects in a scanned image of a first printed sheet, and a second separation distance is determined between a second pair of defects in a scanned image of a second printed sheet. The defects are determined as having a separation distance in that the first and second separation distances are each within a first threshold of a specified separation distance and/or a difference between the first and second separation distances is below a second threshold. A source of the defects can be determined based on the characteristic separation distance.